ExploreASL/ExploreASL: ExploreASL v1.7.0
Bibliographic record
Abstract
ExploreASL v1.7.0 Versions included software Versions included & used third-party tools (see /External/README_SPM.txt): SPM12 7219 CAT12 r1615 LST 2.0.15 Feature improvements Issue #455: Automatically compare results of TestDataSets with a saved reference results Issues #480, #623, #649, #661: Restructure x: x.opts (input arguments and their derivatives), x.dir (directories), x.settings (mostly booleans for pipeline settings), x.dataset (dataset related fields), x.external, ... Issue #572: Restructure JSON handling during NiFTI to BIDS import Issue #580: Add parsing of Gold Standard Phantoms ASL-DRO Issues #588, #612: ExploreASL reads folders and automatically searches for sourceStructure, studyPar and dataPar JSON files Issue #600: Put participants.tsv to the derivatives folder during import to legacy ExploreASL format Issue #602: Remove option for cloning the NIfTI output after import as BIDS directory is a read-only archive Issue #603: Give ExploreASL version in JSON files after BIDS to Legacy conversion Issue #631: Remove repeated warnings Issue #632: Add comparison script for untouched NIfTI comparison Issue #643: bids.layout: avoid printing the same warning repetitively in case multiple scans in a data set have the same issue Issue #656: Improve warnings (data loading) Bug fixes Issue #583: Proper testing of flavors using ExploreASL_Master Issue #584: Print the subject name depending on the existence of its definition in x.SUBJECT to avoid crashes for error reporting in the population module Issue #586: Avoid crashing xASL_adm_GetPopulationSessions if no sessions are found Issue #591: MultiTE import puts TE before PLD in the time series and corrects the JSON output Issue #618: Add session name to all M0Check and ASLCheck QC files in the Population folder Issue #620: xASL_adm_GzipAllFiles: Allow spaces in an input path for macOS/Linux Issue #625: Fix bug related to session format Issue #627: Remove a BIDS fiels and BIDS2Legacy should crash and show you why it crashed Issue #628: Fix parsing sessions and runs for converting rawdata to derivatives Issue #630: Move creation of population folder Issue #646: Improve BIDS warnings Issue #652: xASL_vis_CreateVisualFig: allow empty overlays Issue #659: xASL_stat_PrintStats: Fix visits bug (legacy format) Issue #655: xASL_adm_GetPopulationSessions gave incorrect warnings Issue #666: Warning when multiple dataPar*.json or studyPar*.json or sourcestructure*.json are present Issue #670: Fix warnings and behavior of ExploreASL_Initialize Other improvements Issue #465: Add projects to acknowledgments Issue #615: Add change log to documentation Issue #637: Restyle ExploreASL change log
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.311 | 0.271 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".